高分辨率中国CMIP6统计降尺度气候预估数据集1979-2100（HiCPC）

HiCPC (High-resolution CMIP6 downscaled daily Climate Projections over China) 数据集为清华大学地球系统科学系罗勇教授团队研制，由CMIP6全球模式经降尺度和误差校正后得到的中国范围的气候预估数据集，包括日降水、地表日平均和最高、最低气温4个变量，涵盖气候模式历史期（1979-2014）和未来（2015-2100） SSP1-2.6、SSP2-4.5、SSP3-7.0和SSP5-8.5四种情景下的模式降尺度结果。水平空间分辨率为0.1°，时间分辨率为1天（为方便使用，同时提供了逐月数据）。该数据集是以22个CMIP6全球模式为输入，采用改进的日BCSD方法对历史期和未来预测结果进行校正。历史观测资料来自于CMFD高分辨率历史气象数据集。校正后降水均值平均偏差小于0.1 mm/d,气温平均偏差小于0.4℃，较原始模式结果有较大改善。 HiCPC数据集未来将随着更多模型数据的完成而不断扩展，也将持续更新更多的变量（如近地面风、湿度、长短波辐射等）。该数据集为研究人员和政策制定者提供了更细致的空间尺度，帮助其了解未来潜在的气候变化。

HiCPC (High-resolution CMIP6 downscaled daily Climate Projections over China) dataset was developed by the research team led by Professor Luo Yong from the Department of Earth System Science, Tsinghua University. It is a China-domain climate projection dataset generated via downscaling and bias correction of CMIP6 global climate models, including four core variables: daily precipitation, daily surface mean, maximum and minimum air temperatures. The dataset covers both the historical period (1979–2014) and future periods (2015–2100) under four Shared Socioeconomic Pathway (SSP) scenarios: SSP1-2.6, SSP2-4.5, SSP3-7.0 and SSP5-8.5. It has a horizontal spatial resolution of 0.1° and a daily temporal resolution; monthly aggregated data are also provided for user convenience. This dataset utilizes 22 CMIP6 global models as input, and employs an improved daily BCSD (Bias-Corrected Spatial Disaggregation) method to calibrate the historical and future model outputs. The historical observational reference data are sourced from the CMFD high-resolution historical meteorological dataset. Post-correction, the average bias of precipitation is less than 0.1 mm/d, while the average bias of air temperatures is below 0.4℃, representing a substantial improvement over the original CMIP6 model results. The HiCPC dataset will be continuously expanded as more model data become available, and will also be updated with additional variables such as near-surface wind, humidity, shortwave and longwave radiation. This dataset provides researchers and policymakers with finer spatial scales to facilitate the analysis of potential future climate changes.

搜集汇总

背景与挑战

背景概述

该数据集是由清华大学团队基于CMIP6全球模式降尺度和误差校正生成的中国范围高分辨率气候预估数据，覆盖1979-2100年，包括日降水、气温等4个变量，空间分辨率为0.1°，提供日值和月值数据。它涵盖历史期和未来四种SSP情景，通过校正显著提高了精度，适用于气候变化研究和政策制定。